改进的模型参考自适应在磨矿过程中的应用

doi:10.16182/j.issn1004731x.joss.201804049

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (4): 1608-1614.doi: 10.16182/j.issn1004731x.joss.201804049

改进的模型参考自适应在磨矿过程中的应用

周颖^1,2, 陈阳¹, 岳彬¹

1. 河北工业大学控制科学与工程学院,天津 300130;
2. 河北省控制工程技术研究中心,天津 300130

收稿日期:2016-04-26 修回日期:2016-07-20 出版日期:2018-04-08 发布日期:2019-01-04
作者简介:周颖(1971-),女,河北,博士,副教授,研究方向为智能控制与模式识别;陈阳(1990-),男,河北,硕士,研究方向为计算机过程控制。
基金资助:
河北省教育厅重点项目(ZD2016071)

Improved Model Reference Adaptive and Its Application in Grinding Process

Zhou Ying^1,2, Chen Yang¹, Yue Bin¹

1. College of Control Science and Engineering,Hebei University of Technology, Tianjin 300130, China;
2. ChinaHebei Control Engineering Research Center, Tinajin 300130, China

Received:2016-04-26 Revised:2016-07-20 Online:2018-04-08 Published:2019-01-04

摘要/Abstract

摘要： 针对磨矿过程中一段球磨机与分级机之间存在的多变量、强耦合、参数时变等特性,将其数学模型解耦后,提出一种以差分进化算法在线调整自适应增益的模型参考自适应控制方法,因返砂和给水等因素给被控对象模型带来不确定性,通过差分进化不断在线调整自适应增益,以克服磨矿运行过程参数变化和各种干扰对系统的影响。仿真结果表明,与以梯度法为自适应律的模型参考控制效果相比,该控制方法具有超调小、响应时间快、鲁棒性能好等特点,实现分级机溢流浓度的稳定控制,证明基于差分进化算法的模型参考自适应控制方法的有效性和实用性。

关键词: 差分进化, 参考模型, 自适应控制, 解耦

Abstract: Aiming at the characteristics between a ball mill and a grader during the grinding process such as multi variable, strong-coupling and parameter time variation, after decoupling the mathematical model, a model reference adaptive control (MRAC) method for online automatic gain adjustment by differential evolution algorithm is proposed. Due to return sand and water supply and other factors bringing uncertainty to the controlled object model, it needs to adjust constantly adaptation gain online through the differential evolution so as to overcome the effects of parameter variations in mine operation process and various kinds of interference to system. Simulation results show that the control method performs a smaller overshoot, quicker responsivity and better robustness than the model reference adaptive control with gradient method; the stable control of the grader overflow concentration is achieved; and the model reference adaptive control method with differential evolution algorithm is effective and practical.

Key words: differential evolution, reference model, adaptive control, decoupling

中图分类号:

TP181

周颖, 陈阳, 岳彬. 改进的模型参考自适应在磨矿过程中的应用[J]. 系统仿真学报, 2018, 30(4): 1608-1614.

Zhou Ying, Chen Yang, Yue Bin. Improved Model Reference Adaptive and Its Application in Grinding Process[J]. Journal of System Simulation, 2018, 30(4): 1608-1614.

参考文献

[1] 卢绍文, 余策. 磨矿粒度动态过程的一种快速Monte Carlo仿真方法[J]. 自动化学报, 2014, 40(9): 1903-1911.
Lu Shaowen, Yu Ce.A Fast Monte Carlo Algorithm for Dynamic Simulation of Particle Size Distribution of Grinding Processes[J]. Acta Automatica Sinica, 2014, 40(9): 1903-1911.
[2] Jian Tang, Tianyou Chai, Wen Yu, et al.Modeling Load Parameters of Ball Mill in Grinding Process Based on Selective[J]. IEEE transactions on automation science and engineering(S1545-5955), 2013, 10(3): 726-740.
[3] 周平. 磨矿过程运行反馈控制[M]. 北京: 科学出版社, 2015: 50-51.
Zhou Ping.Operational feedback control for Grinding Process[M]. Beijing: Science Press, 2015: 50-51.
[4] 刘潇. 多目标差分进化混合算法研究及其在磨矿分级中的应用[D]. 长沙: 中南大学, 2011.
Liu Xiao.Research of a Hybrid DE Algorithm for Multi-objective Optimization Problems and its Application in Grinding and Classification Process[D]. Changsha: Central South University, 2011.
[5] 赵大勇, 柴天佑. 自适应内模控制方法在磨矿过程中的应用[J]. 控制工程, 2009, 16(4): 426-428.
Zhao Dayong, Chai Tianyou.Adaptive Internal Model Control Method and Its Application to Grinding Process[J]. Control Engineering, 2009, 16(4): 426-428.
[6] 任金霞, 王挺. 基于改进型粒子群优化的磨矿分级系统溢流浓度控制的研究[J]. 矿山机械, 2012, 9(5): 92-95.
Ren Jinxia, Wang Ting.Study on over? ow density control of milling and classiﬁ cation system based on improved particle swarm optimization[J]. Micollurgical Machinery, 2012, 9(5): 92-95.
[7] Shaowen Lu, Ping Zhou, Tianyou Chai, et al.Modeling and Simulation of Whole Ball Mill Grinding Plant for Integrated Control[J]. IEEE transactions on automation science and engineering(S1545-5955), 2014, 11(4): 1004-1019.
[8] 赵大勇, 岳恒, 周平, 等. 基于智能优化控制的磨矿过程综合自动化系统[J]. 山东大学学报, 2005, 35(3): 119-124.
ZHAO Dayong, YUE Heng, ZHOU Ping, et al.Integrated Automation System of Grinding Process Based on Intelligent Optimizing Control[J] .Journal of Shandong University, 2005, 35(3): 119-124.
[9] 李勇. 磨矿过程参数软测量与综合优化控制的研究[D]. 大连: 大连理工大学, 2006.
Li Yong.Research on the Parameters’ Soft Sensing and Synthetic Optimizing Control of Grinding Process [D]. Dalian: Dalian University of Technology, 2006.
[10] 杨燕, 曹建光, 田志宏, 等. 交流电机模型参考自适应控制系统的仿真[J]. 电气传动, 2007, 37(5): 13-15.
Yang Yan, Cao Jianguang, Tian Zhihong, et al.Simulation of Model Reference Adaptive Control System in AC Motor Control[J]. Electric Drives, 2007, 37(5): 13-15.
[11] Yongming Li, Shaocheng Tong.Adaptive Fuzzy Output-Feedback Control of Pure-Feedback Uncertain Nonlinear Systems With Unkonwn Dead Zone[J]. IEEE transaction on fuzzy systems(S1064-1246), 2014, 22(5): 1341-1347.
[12] 戈剑武, 祁荣宾, 钱锋, 等. 一种改进的自适应差分进化算法[J]. 华东理工大学学报, 2009, 35(4): 600-605.
Ge Jianwu, Qi Rongbin, Qian Feng, et al.A Modified Adaptive Differential Evolution Algorithm[J]. Journal of East China University of Science and Technology, 2009, 35(4): 600-605.
[13] 杨启文, 蔡亮, 薛云灿, 等. 差分进化算法综述[J]. 模式识别与人工智能, 2008, 21(4): 506-513.
Yang Qiwen, Cai Liang, Xue Yuncan, et al.A Survey of Differential Evolution Algorithms[J]. Journal of Pattern Recognition and Artificial Intelligence, 2008, 21(4): 506-513.
[14] Nyambayar Baatar, Dianhai Zhang, Chang-Seop Koh, et al.An Improve Differential Evolution Algorithm Adopting λ-Best Mutation Strategy for Global Optimization of Electromagnetic Devices[J]. IEEE transactions on Magazines(S0018-9464), 2013, 49(5): 2097-2010.
[15] 王朋, 刘林, 陈哲, 等. 基于改进DE算法的PID参数整定及其应用[J]. 动力工程学报, 2015, 35(3): 191-196.
Wang Peng, Liu Lin, Chen Zhe, et al.PID Parameter Tuning Based on Improved DE Algorithm and the Application[J]. Journal of Chinese Society of Power Engineering, 2015, 35(3): 191-196.

改进的模型参考自适应在磨矿过程中的应用

Improved Model Reference Adaptive and Its Application in Grinding Process

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李晖, 云昊, 岳红丽. 基于SVM逆系统的PMSM云模型整定PID控制[J]. 系统仿真学报, 2021, 33(8): 1846-1855.
[2]	余伟, 梁恒辉, 罗映. 一种改进差分进化算法的分数阶系统辨识研究[J]. 系统仿真学报, 2021, 33(5): 1157-1166.
[3]	马立新, 朱勇杰, 季乐延. 神经网络优化的无感永磁同步电机控制系统[J]. 系统仿真学报, 2021, 33(3): 622-630.
[4]	修素朴, 文元桥, 肖长诗, 元海文, 詹文强. 倾转四旋翼自主降落的设计与仿真[J]. 系统仿真学报, 2020, 32(9): 1676-1685.
[5]	孙灿, 周新宇, 王明文. 一种融合邻域搜索的多策略差分进化算法[J]. 系统仿真学报, 2020, 32(6): 1071-1084.
[6]	梅盼盼, 吴亮红, 张红强, 王慧莹. 含风电电力系统动态经济调度的差分纵横交叉优化[J]. 系统仿真学报, 2020, 32(6): 1179-1187.
[7]	周颖, 贾巧娟, 张燕, 常明新. 改进的逆解耦自抗扰内模控制在磨矿中的应用[J]. 系统仿真学报, 2020, 32(5): 837-847.
[8]	王艳, 丁宇. 动态柔性作业车间优化调度与决策方法[J]. 系统仿真学报, 2020, 32(11): 2073-2083.
[9]	李琳, 林燕龙, 邹焱飚. Scara机器人关节摩擦建模与补偿的实验与仿真[J]. 系统仿真学报, 2019, 31(8): 1572-1581.
[10]	董泽, 马宁. 差分量子粒子群算法的分数阶混沌系统参数估计[J]. 系统仿真学报, 2019, 31(8): 1664-1673.
[11]	林雨谷, 王艳. 离散车间能效数据挖掘及调度优化[J]. 系统仿真学报, 2019, 31(12): 2702-2711.
[12]	王杰, 陈彬, 袁鹏, 马亮, 邱晓刚. 数据驱动的最优互惠避碰模型偏好速度研究[J]. 系统仿真学报, 2019, 31(12): 2731-2739.
[13]	蒋清扬, 宋华. 航空发动机换热器控制技术研究[J]. 系统仿真学报, 2019, 31(11): 2442-2451.
[14]	熊印国. 相对变换KPCA的变压器油击穿电压预测建模[J]. 系统仿真学报, 2018, 30(5): 1657-1664.
[15]	张静华, 韩璞. 多算法多种群协同优化差分进化算法[J]. 系统仿真学报, 2018, 30(5): 1690-1699.